Optimized XGBoost Model with Whale Optimization Algorithm for Detecting Anomalies in Manufacturing

Surjeet Dalal; Uma Rani; Umesh Kumar  Lilhore; Neeraj Dahiya; Reenu Batra; Nasratullah Nuristani; Dac-Nhuong  Le

doi:10.47852/bonviewJCCE42023545

Authors

Surjeet Dalal Department of Computer Science and Engineering, Amity University Haryana Gurugram, India https://orcid.org/0000-0002-4325-9237
Uma Rani Department of Computer Science and Engineering, World College of Technology and Management, India
Umesh Kumar Lilhore Department of Computer Science and Engineering, Galgotias University, India
Neeraj Dahiya Department of Computer Science and Engineering, SRM University Delhi-NCR, India
Reenu Batra Department of Computer Science and Engineering, Global Institute of Technology and Management, India https://orcid.org/0000-0001-5787-2377
Nasratullah Nuristani Department of Spectrum Management, Afghanistan Telecommunication Regulatory Authority, Afghanistan
Dac-Nhuong Le Faculty of Information Technology, Haiphong University, Vietnam https://orcid.org/0000-0003-2601-2803

DOI:

https://doi.org/10.47852/bonviewJCCE42023545

Keywords:

XGboost, whale optimization algorithm (WOA), anomalies detection, manufacturing, industry 4.0

Abstract

Anomalies and defects in the manufacturing process hinder operating efficiency and product quality. The Whale Optimization Algorithm (WOA) optimizes the XGBoost model for better anomaly identification by iteratively refining hyperparameters. Experiments using real-world manufacturing datasets prove proposed model works. Comparing the proposed model to traditional anomaly detection methods shows its superior performance in industry patent concept. The optimized XGBoost model's interpretability and anomaly detection features are also discussed. In this paper, WOA is applied in this work to optimize hyperparameters of XGBoost, a robust gradient boosting technique for accurate anomaly detection in manufacturing systems. Optimized XGBoost gained 1.00 precision value, 0.9 recall value and 0.96 f1-score for class 0.0 and gained a 0.95 precision value, 1.00 recall value, and a 0.97 f1-score for class 1.0. The proposed model gained 0.993 Train Score and 0.964 Test Score. Our findings suggest that integrating XGBoost with the WOA may uncover manufacturing process irregularities. Optimization improves detection accuracy and provides a flexible and interpretable framework, helping modern industrial processes maintain quality and efficiency. This research encourages machine learning optimization for industrial patent applications, advancing anomaly detection methods.

Received: 2 June 2024 | Revised: 29 August 2024 | Accepted: 27 September 2024

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data available on request from the corresponding author upon reasonable request.

Author Contribution Statement

Surjeet Dalal: Conceptualization, Validation, Writing – original draft, Project administration. Uma Rani: Conceptualization, Formal analysis, Writing – review & editing. Umesh Kumar Lilhore: Methodology, Investigation, Resources, Writing – original draft. Neeraj Dahiya: Methodology, Data curation, Writing - review & editing. Reenu Batra: Software, Visualization, Supervision. Dac-Nhuong Le: Validation, Supervision, Project administration.